The need for XAI methods has prompted the research community to develop a bewildering number of different approaches to unraveling the black boxes of AI models. A considerable part of these approaches is based on telling the user of the XAI system in various ways which features of the input data are important for a decision (often called Feature Attribution) [1]}. Other methods, which are closer to human habits of explanation, are based on the paradigm of Counterfactual Thinking [2]}.
Procedures that follow this guiding principle try answering the question of What if...? by showing an alternative reality and the corresponding decision of the AI.
Here, in contrast to feature attribution mechanisms, not only the importance of the various features is emphasized. Rather, it is conveyed, even if only indirectly, why features are relevant.
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